AI For Human Voice Recognition Is A Reality Today (Datasets And Models For Recognizing Emotions)


Introduction

Sound technology is one step ahead. We used to be known for giving voice commands to artificial intelligence (AI) robots to control smart devices in our cars, smart home systems, or voice translation applications. Artificial intelligence is now being further developed to understand and classify emotions from speech patterns and to better respond to human emotional stimuli.

Speech emotion recognition intelligence includes many applications, such as a call center system that can check the status of customers calling for service when they are upset or overloaded, and record their feelings from the tone of whole callers as statistics from dissatisfied customers. "Artificial intelligence, which can express more natural emotions when talking to users, can also be created to replace the monotonous, robotic sound we know." funded by Digital Economy Promotion Agency (depot) and Advanced Info Service, Public Co., Ltd (AIS).

Emotion classification in the wider AI application

The ability to use Speech Recognition models in many types of work according to the users' ideas of what they want from mood analysis. "The use is not limited to computer workers. We need to look at which users want to use emotional ratings. Call centers can use it, for example, to rate angry customers and to analyze the topics to which customers respond the most." and what they can do. they can also be avatars or AI robots with facial expressions and moving lips and can respond to users. "

"In the future, we plan to develop it for use in mental health in depressed patients so that robots can respond emotionally to humans."

Natural language processing datasets

Natural language processing (NLP) refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI—concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.

This paper is divided into 5 parts; they are:
  • Text classification
  • Language modeling
  • Image description
  • Machine translation
  • Word recognition
Let's start.

1. Text classification

Text classification refers to the marking of sentences or documents, such as the classification of e-mail spam and sentiment analysis.

Below are some good sets of introductory text reviews.

Reuters Newswire (Reuters-21578). A collection of news documents appeared on Reuters in 1987 indexed by category.
IMDB Movie Review Sentiment Classification (stanford). A collection of movie reviews from imdb.com and their positive and negative sentiment.
Film news Group Sentiment Classification (cornell). A collection of movie reviews from imdb.com and their positive and negative sentiment. More information can be found in the article:

Single-label text categorization datasets.

2. Language modeling

Language modeling is about creating a statistical model to predict the next word in a sentence or another letter in a word, whichever precedes it. This is a pre-cursor task with tasks such as language recognition and machine translation.

This is a pre-cursor task with tasks such as language recognition and machine translation. Below are some good datasets for language modeling for beginners.

A large collection of free books available in plain text for various languages.
There are more formal corpora that have been well studied; for example:

3. Description of Images

Image captioning is the task of creating a text description of the image. Below are good starting dates for captioning images.


4. Machine translation

Machine translation is the task of translating text from one language to another.

Below are some good datasets for machine translation for beginners.

Hansard was associated with the 36th Parliament of Canada. A few sentences in English and French.
European Parliament Parallel Corpus Procedures 1996-2011. Word pairs in a set of European languages.
Lots of standard datasets are used for annual machine translation challenges; look:

5. Know the Word

Speech recognition is the task of converting the sound of spoken language into human-readable text. Here are some good facts about starting language recognition.

Below are some good datasets for summarizing documents for beginners.

Data set in legal case reports. A set of 4,000 legal cases and their summaries. TIPSTER Text Summary Evaluation conference corpus. Collection of almost 200 documents and their summaries.
AQUAINT corpus in English news text. It is not free, but it is widely used. Series of news articles. More information:

Where Can I Find Good Datasets For Artificial Intelligence Model Training?

Global Technical Solutions (GTS) provides you with all the Quality Datasets you could possibly need to power your technology in whatever dimension of speech, language, or voice function you would want. We have the means and expertise to handle any project relating to constructing a natural language corpus, truth data collection, semantic segmentation, and transcription. We can help tailor your technology to suit any region or locality in the world, we have a vast collection of data and a robust team of experts.

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